The genetic architecture of differentiating behavioural and emotional problems in early life
Bibliographic record
Abstract
Abstract Early in life, behavioural and cognitive traits associated with risk for developing a psychiatric condition are broad and undifferentiated. As children develop, these traits differentiate into characteristic clusters of symptoms and behaviours that ultimately form the basis of diagnostic categories. Understanding this differentiation process - in the context of genetic risk for psychiatric conditions, which is highly generalised - can improve early detection and treatment. We modelled the differentiation of behavioural and emotional problems from age 1.5-5 years (behavioural problems – emotional problems = differentiation score) in a pre-registered study of ~79 000 children from the population-based Norwegian Mother, Father, and Child Cohort Study. We used genomic structural equation modelling to identify genetic signal in differentiation and the total level of behavioural and emotional problems, investigating their links with 11 psychiatric and neurodevelopmental conditions. We examined associations of polygenic scores (PGS) with differentiation and total problems and assessed the relative contributions of direct and indirect genetic effects in over 33 000 family trios. Differentiation exhibited detectable common variant heritability (h2SNP = 0.023 [0.017, 0.029]), and was primarily genetically correlated with psychiatric conditions via a “neurodevelopmental” factor. PGS analyses revealed a substantial association between polygenic liability to ADHD and differentiation (β = 0.09 [0.08, 0.11]), and a weaker association with total problems (β = 0.05 [0.04, 0.06]). Trio-PGS analyses indicated predominantly direct genetic effects on both outcomes. We uncovered systematic genomic signal in the differentiation process, mostly related to common variants associated with neurodevelopmental conditions. Investigating the co-occurrence and differentiation of behavioural and emotional problems may enhance our ability to detect and eventually prevent the emergence of psychiatric conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".